Reduced-order modeling of linear time invariant systems using big bang big crunch optimization and time moment matching method

• A new frequency domain model order reduction scheme is proposed.
• The scheme unifies the concept of time-moment matching and big bang big crunch algorithm.
• The proposed scheme is applicable for SISO, MIMO and time-delayed systems.
• Performance of proposed method is better than conventional reduction technique.

In this paper, a new approach is proposed to approximate the high-order linear time invariant (LTI) system into its low-order model. The proposed approach is a mixed method of model order reduction scheme consisting of recently developed big bang big crunch optimization algorithm and the time-moment matching method. This proposed method is applicable to single-input single-output, multi-input multi-output system, and time delayed LTI systems. The proposed approach is substantiated with various numerical examples of low and high-order systems. The results show that the reduced-order models preserve both transient and steady state conditions of original systems. Further, the results are also compared with the existing approaches of reduced order modeling which show exceptional improvement in integral square error (ISE) and other time domain specifications.